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含持续与暂时无效率的半参数随机前沿模型贝叶斯估计AI 解读

We propose kernel estimators for the densities of persistent and transient inefficiencies in a generalized panel data stochastic frontier model. This approach relaxes the conventional distributional assumptions imposed on the inefficiencies, thereby increasing robustness. A Bayesian sampling algorithm is developed to estimate all model parameters and unobserved entities, as well as the bandwidths needed to construct the densities of persistent and transient inefficiencies.…

部分参数模型中的联合估计与带宽选择AI 解读

ABSTRACT We propose a single‐step approach to estimating a model with both a known nonlinear parametric component and an unknown nonparametric component. We study the large sample behavior of a simultaneous optimization routine that estimates both the parameter vector of the parametric component and the bandwidth vector used to smooth the unknown function. In our application, we estimate a partially constant elasticity of substitution production function and uncover results…

弱可分随机前沿模型的非参数估计AI 解读

We propose a robust and versatile approach to estimate the stochastic frontier model which avoids parametric assumptions. Our approach requires a single continuous covariate which monotonically influences the conditional mean of inefficiency. Subject to these conditions, the frontier and the conditional mean of inefficiency can be estimated nonparametrically. The estimator we propose uses local least squares and marginal integration making it easy to implement across…

含零无效率质量点的随机前沿惩罚筛估计AI 解读

Summary Stochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption.…

用于前沿效率分析的贝叶斯人工神经网络AI 解读

Artificial neural networks have offered their share of econometric insights, given their power to model complex relationships. One area where they have not been readily deployed is the estimation of frontiers. The literature on frontier estimation has seen its share of research comparing and contrasting data envelopment analysis (DEA) and stochastic frontier analysis (SFA), the two workhorse estimators. These studies rely on both Monte Carlo experiments and actual data sets…

似乎不相关回归系统的平滑系数估计AI 解读

This paper proposes estimation and inference for the semiparametric smooth coefficient seemingly unrelated regression model. We discuss the imposition of cross-equation restrictions which are required by economic theory as well as methods for data driven bandwidth selection. A test of correct functional form for the entire system of equations is also constructed. Asymptotic and finite sample results are given. We illustrate our estimator by applying it to a cost system for…

基于梯度的非参数回归平滑参数选择AI 解读

Estimating gradients is of crucial importance across a broad range of applied economic domains. Here we consider data-driven bandwidth selection based on the gradient of an unknown regression function. This is a difficult problem given that direct observation of the value of the gradient is typically not observed. The procedure developed here delivers bandwidths which behave asymptotically as though they were selected knowing the true gradient. Simulated examples showcase…

允许零无效率的随机前沿模型AI 解读

Traditional stochastic frontier models impose inefficient behavior on all firms in the sample of interest. If the data under investigation represent a mixture of both fully efficient and inefficient firms then off-the-shelf frontier models are statistically inadequate. We introduce the zero inefficiency stochastic frontier model which can accommodate the presence of both efficient and inefficient firms in the sample. We derive the corresponding log-likelihood function,…

模型平均方法比较：评估经济增长的决定因素AI 解读

SUMMARY This paper investigates the replicability of three important studies on growth theory uncertainty that employed Bayesian model averaging tools. We compare these results with estimates obtained using alternative, recently developed model averaging techniques. Overall, we successfully replicate all three studies, find that the sign and magnitude of these new estimates are reasonably close to those produced via traditional Bayesian methods and deploy a novel strategy to…

第一价格拍卖的贝叶斯估计方法AI 解读

This paper considers Bayesian estimation strategies for first-price auctions within the independent private value paradigm. We develop an ‘optimization’ error approach that allows for estimation of values assuming that observed bids differ from optimal bids. We further augment this approach by allowing systematic over or underbidding by bidders using ideas from the stochastic frontier literature. We perform a simulation study to showcase the appeal of the method and apply…

非参数第一价格拍卖模型的实证实施AI 解读

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparametric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in…

众数、加权众数与校准众数：基于多峰性检验的聚类证据AI 解读

Abstract We apply recent results from the statistics literature to test for multimodality of worldwide distributions of several (unweighted and population‐weighted) measures of labor productivity. Specifically, we employ Silverman (Bump) and Dip modality tests, calibrated to correct for their incorrect asymptotic levels. We show that test results are sensitive to the test statistic employed and to population weighting. But regardless of the statistical criterion used,…

特征价格函数的非参数估计AI 解读

Abstract Rosen's ( 1974 ) theory of hedonic prices is implemented econometrically using recently developed nonparametric techniques to examine the influence of qualitative factors on the price of a house. Our ability to smooth categorical variables leads to greater generalization in the valuation process and provides a canvas for interactions between categorical and continuous variables that is difficult to exploit in parametric and semiparametric models. This is illustrated…